Boolean indexing in Pandas - Mini Project: Build & Apply

Boolean Indexing with pandas

📖 Scenario: You work in a small store and have a list of products with their prices. You want to find which products cost more than $20 to decide what to promote.

🎯 Goal: Use boolean indexing in pandas to filter products that cost more than $20.

📋 What You'll Learn

Create a pandas DataFrame with product names and prices

Create a boolean condition to select products with price greater than 20

Use boolean indexing to filter the DataFrame

Print the filtered DataFrame

💡 Why This Matters

🌍 Real World

Stores often need to filter products by price to create promotions or discounts.

💼 Career

Data analysts use boolean indexing in pandas to quickly filter and analyze data based on conditions.

1

Create the products DataFrame

Import pandas as pd and create a DataFrame called products with these exact entries: product names 'Pen', 'Notebook', 'Eraser', 'Backpack', and prices 5, 25, 3, 45 respectively.

Pandas

import pandas as pd
# Create the DataFrame called products with the given product names and prices
# Your code here

Need a hint?

Use pd.DataFrame with a dictionary containing keys 'product' and 'price'.

2

Create a boolean condition for prices over 20

Create a variable called expensive that holds a boolean Series selecting rows from products where the price is greater than 20.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
# Create the boolean Series expensive for prices > 20
# Your code here

Need a hint?

Use products['price'] > 20 to create the boolean Series.

3

Filter products using boolean indexing

Create a new DataFrame called expensive_products by using boolean indexing on products with the expensive variable.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
expensive = products['price'] > 20
# Use boolean indexing to create expensive_products DataFrame
# Your code here

Need a hint?

Use products[expensive] to filter the rows.

4

Print the filtered DataFrame

Print the expensive_products DataFrame to show only products with price greater than 20.

Pandas

import pandas as pd
products = pd.DataFrame({
    'product': ['Pen', 'Notebook', 'Eraser', 'Backpack'],
    'price': [5, 25, 3, 45]
})
expensive = products['price'] > 20
expensive_products = products[expensive]
# Print the expensive_products DataFrame
# Your code here

Need a hint?

Use print(expensive_products) to display the filtered DataFrame.